Systems and methods for extending the battery life of a wireless sensor in a building control system

ABSTRACT

A building control system includes a wireless measurement device and a controller. The wireless measurement device measures a plurality of values of an environmental variable and uses the plurality of measured values to predict one or more future values of the environmental variable. The wireless device periodically transmits, at a transmission interval, a message that includes a current value of the environmental variable and the one or more predicted values of the environmental variable. The controller receives the message from the wireless device and parses the message to extract the current value and the one or more predicted future values of the environmental variable. The controller periodically and sequentially applies, at a controller update interval shorter than the transmission interval, each of the extracted values as an input to a control algorithm that operates to control the environmental variable.

BACKGROUND

The present invention relates generally to methods for managing buildingsystems. The present invention relates more particularly to systems andmethods for conserving power when transmitting sensor data to a buildingmanagement system.

A building management system (BMS) is, in general, a system of devicesconfigured to control, monitor, and manage equipment in or around abuilding or building area. A BMS can include, for example, a HVACsystem, a security system, a lighting system, a fire alerting system,any other system that is capable of managing building functions ordevices, or any combination thereof. Wireless devices such as wirelesssensors may use a large portion of battery life transmitting data.

Conventional methods of transmitting data from wireless sensors mayinclude measuring and transmitting one value at a time. The collectionand transmission interval may align with the controller update period,providing a responsive system. However, battery life may diminishquickly. It would be desirable to provide a method for wirelesslytransmitting sensor data which overcomes the disadvantages ofestablished methods.

SUMMARY

One implementation of the present disclosure is a building controlsystem which includes a wireless measurement device. The wirelessmeasurement device includes a sensor that measures a plurality of valuesof an environmental variable at a location of the wireless device, apredictor that uses the plurality of measured values of theenvironmental variable to predict one or more future values of theenvironmental variable, and a first wireless radio that periodicallytransmits, at a transmission interval, a message comprising a currentvalue of the environmental variable and the one or more predicted valuesof the environmental variable. The building control system also includesa controller. The controller includes a second wireless radio thatreceives the message from the wireless device, a message parser thatparses the message to extract the current value and the one or morepredicted future values of the environmental variable, and a feedbackcontroller that periodically and sequentially applies, at a controllerupdate interval shorter than the transmission interval, each of theextracted values as an input to a control algorithm that operates tocontrol the environmental variable.

In some embodiments, the wireless measurement device periodicallymeasures the environmental variable at a measurement interval shorterthan the transmission interval. In other embodiments, the wirelessmeasurement device compares an actual measured value of theenvironmental variable with a previously-predicted value of theenvironmental variable, and transmits the message to the controller inresponse to a determination that the actual measured value differs fromthe previously-predicted value.

In some embodiments, the wireless measurement device predicts a futurevalue of the environmental variable for each controller update intervalwithin the current transmission interval.

In other embodiments, the wireless measurement device compares a currentmeasured value of the environmental variable with a previous measuredvalue of the environmental variable, determines a rate of change of theenvironmental variable between the current and previous measured value,and transmits the message to the controller in response to adetermination that the rate of change exceeds a threshold.

In some embodiments, the wireless measurement device compares an actualmeasured value of the environmental variable at a particular time with apreviously-predicted value of the environmental variable for the giventime, determines whether a difference between the actual measured valueand the previously-predicted value exceeds a threshold, and transmitsthe message to the controller in response to a determination that thedifference between the actual measured value and thepreviously-predicted value exceeds the threshold.

In some embodiments, the wireless measurement device compares an actualmeasured value of the environmental variable with at least one of apreviously-predicted value of the environmental variable and apreviously-measured value of the environmental variable, and redefines aprediction algorithm used by the predictor in response to at least oneof: a difference between the actual measured value of the environmentalvariable and the previously-predicted value of the environmentalvariable exceeding a threshold, and a rate of change of the value of theenvironmental variable exceeding a threshold.

Another implementation of the present disclosure is a method forcontrolling an environmental variable in a building control system. Themethod includes measuring a plurality of values of the environmentalvariable using a sensor of a wireless measurement device and predictingone or more future values of the environmental variable based on theplurality of measured values. The method also includes periodicallytransmitting, at a transmission interval, a message from the wirelessmeasurement device to a controller, the message comprising a currentvalue of the environmental variable and the one or more predicted valuesof the environmental variable. The method includes parsing the messageat the controller to extract the current value and the one or morepredicted future values of the environmental variable and periodicallyand sequentially applying, at a controller update interval shorter thanthe transmission interval, each of the extracted values as an input to acontrol algorithm that operates to control the environmental variable.

In some embodiments, measuring the plurality of values of theenvironmental variable includes periodically measuring the environmentalvariable at a measurement interval shorter than the transmissioninterval. In other embodiments, the method further includes comparing acurrent measured value of the environmental variable with a previousmeasured value of the environmental variable, determining a rate ofchange of the environmental variable between the current and previousmeasured value, and transmitting the message to the controller inresponse to a determination that the rate of change exceeds a threshold.

In some embodiments, predicting the environmental variable includespredicting a future value of the environmental variable for eachcontroller update interval within the current transmission interval. Inother embodiments, the method further includes comparing an actualmeasured value of the environmental variable at a particular time with apreviously-predicted value of the environmental variable for the giventime, determining whether a difference between the actual measured valueand the previously-predicted value exceeds a threshold, and transmittingthe message to the controller in response to a determination that thedifference between the actual measured value and thepreviously-predicted value exceeds the threshold.

In some embodiments, the method further includes comparing an actualmeasured value of the environmental variable with a previously-predictedvalue of the environmental variable, and adjusting the transmissioninterval based on one of: a difference between the actual measured valueof the environmental variable and the previously-predicted value of theenvironmental variable, and a rate of change of the value of theenvironmental variable.

In some embodiments, the method further includes comparing an actualmeasured value of the environmental variable with thepreviously-predicted value of the environmental variable, and redefininga prediction algorithm used by the predictor based on one of: adifference between the actual measured value of the environmentalvariable and the previously-predicted value of the environmentalvariable, and a rate of change of the value of the environmentalvariable.

Yet another implementation of the present disclosure is a wirelessmeasurement device which includes a sensor that measures a plurality ofvalues of an environmental variable at a location of the wirelessdevice. The wireless measurement device also includes a predictor thatuses the plurality of measured values of the environmental variable topredict one or more future values of the environmental variable, and afirst wireless radio that periodically transmits, at a transmissioninterval, a message comprising a current value of the environmentalvariable and the one or more predicted values of the environmentalvariable.

In some embodiments, the wireless measurement device is configured toperiodically measure the environmental variable at a measurementinterval shorter than the transmission interval. In some embodiments,the wireless measurement device includes a comparator that compares anactual measured value of the environmental variable with apreviously-predicted value of the environmental variable and determineswhether a difference between the actual measured value and thepreviously-predicted value exceeds a threshold. The wireless measurementdevice also includes a transmission timer that transmits the message toa controller in response to a determination that the actual measuredvalue differs from the previously-predicted value by an amount in excessof the threshold.

In some embodiments, the predictor predicts a future value of theenvironmental variable for each of a plurality of controller updateintervals within the current transmission interval. In some embodiments,the wireless measurement device includes a comparator that compares acurrent measured value of the environmental variable with a previousmeasured value of the environmental variable and determines whether arate of change between the measured values exceeds a threshold. Thewireless measurement device also includes a transmission timer thattransmits the message to a controller in response to a determinationthat the rate of change between the measured values exceeds thethreshold.

In some embodiments, the wireless measurement device includes acomparator that compares an actual measured value of the environmentalvariable with a previously-predicted value of the environmentalvariable. The wireless measurement device also includes a transmissiontimer that adjusts the transmission interval based on one of: adifference between the actual measured value of the environmentalvariable and the previously-predicted value of the environmentalvariable, and a rate of change of the value of the environmentalvariable.

Those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the devices and/orprocesses described herein, as defined solely by the claims, will becomeapparent in the detailed description set forth herein and taken inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a drawing of a building equipped with a HVAC system in whichthe systems and method of the present disclosure may be implemented,according to an exemplary embodiment.

FIG. 2 is a block diagram of a waterside system which may be used inconjunction with the HVAC system of FIG. 1, according to an exemplaryembodiment.

FIG. 3 is a block diagram of an airside system which may be used inconjunction with the HVAC system of FIG. 1, according to an exemplaryembodiment.

FIG. 4 is a block diagram of a building management system in which thesystems and methods of the present disclosure may be implemented,according to an exemplary embodiment.

FIG. 5A is a detailed block diagram of the wireless device and buildingmanagement system controller of FIG. 4, according to an exemplaryembodiment.

FIG. 5B is a detailed block diagram of the wireless device and buildingmanagement system controller of FIG. 4, according to another exemplaryembodiment.

FIG. 6 is a graph illustrating an existing wireless transmission timingprocess, according to an exemplary embodiment.

FIG. 7 is a flowchart of the existing wireless transmission timingprocess of FIG. 6, according to an exemplary embodiment.

FIG. 8 is a graph illustrating a proposed wireless transmission timingprocess, according to an exemplary embodiment.

FIG. 9A is a drawing of the new wireless transmission timing processwhich may be performed by the system of FIG. 4, according to anexemplary embodiment.

FIG. 9B is a flowchart of the new wireless transmission timing processwhich may be performed by the system of FIG. 4, according to anexemplary embodiment.

FIG. 10A is a drawing of another new wireless transmission timingprocess which may be performed by the system of FIG. 4, according to anexemplary embodiment.

FIG. 10B is a flowchart of another new wireless transmission timingprocess which may be performed by the system of FIG. 4, according to anexemplary embodiment.

DETAILED DESCRIPTION

Overview

Referring generally to the FIGURES, a building management system,feedback controller, and components thereof are shown according tovarious exemplary embodiments. At the most fundamental level, feedbackcontrol leverages measurements to make decisions about how to manipulatethe inputs of a system so that the controlled system achieves anexpected or desirable behavior. Traditionally, wired sensors are used infeedback control systems. However, with the increasing availability andcapacity of wireless sensors and wireless communication networks,wireless sensors are becoming a viable alternative to wired sensors.

In a building HVAC system, wireless sensors may be used to monitor avariety of building conditions such as temperature, humidity, pressure,airflow, etc. For example, a wireless temperature sensor may be used tomeasure the temperature of a building zone and send zone temperaturemeasurements to a feedback controller. The controller subsequentlycomputes control inputs that ensure the zone temperature (i.e., themeasured variable) is maintained at a zone temperature setpoint.

While wireless sensors are currently available, many wireless sensorssuffer from a short battery life. One of the major challenges of usingbattery-powered wireless sensors within feedback control applications isthat the battery power consumption required to transmit the measurementto the controller may be significant. However, a wireless sensor may usesignificantly less power when recording measurements than it uses whentransmitting a message to a feedback controller. The present disclosureoffers systems and methods of prediction to reduce the quantity oftransmissions to reduce power consumption and data traffic.

One technique for increasing the battery life of a wireless sensor is todecrease the frequency of transmissions from the wireless sensor to thecontroller. However, many feedback control systems require the measuredvariable be measured (or sampled) and fed back to the controller at asufficiently fast sampling rate relative to the dominant time constantof the system (i.e., the time constant of the dominant dynamic behaviorof the system). For example, for zone temperature control, the zonetemperature may be sampled (i.e., measured) every minute and transmittedto the controller each minute. Sampling the measured variable lessfrequently (i.e., increasing the time interval between consecutivemeasurements) may lead to substantial control performance degradation.In other words, many feedback controllers require a new sample of themeasured variable at relatively short intervals (e.g., a new sample eachminute) to achieve desirable control performance.

Advantageously, the systems and methods described herein can be used toextend the battery life of wireless sensors (and other wireless devicesthat transmit measurements) without sacrificing control performance. Forexample, a wireless measurement device may record measurements of ameasured variable at a regular measurement interval (e.g., onemeasurement per minute). The wireless device may use a series ofmeasurements to predict one or more future values of the measuredvariable. Multiple values may be sent to the controller in a singlemessage each time the wireless device transmits, thereby reducing thenumber of transmissions needed. By incorporating advanced algorithms forpredicting future values accurately, the proposed systems and methods ofthe present disclosure may increase the transmission interval, therebyreducing power consumption and data traffic.

In some embodiments, one message is sent each transmission interval,which may be significantly longer than the measurement interval (e.g.,one message transmission every four minutes). In some embodiments, themessage sent to the feedback controller contains a current value of themeasured variable (e.g., a measured value) and one or more predictedvalues of the measured variable. The predicted values may be based onmultiple measurements and past values stored in memory. A message parseron the controller side may extract the multiple values from the messageand provide the feedback controller with one value from the message eachtime the controller updates its control output (i.e., at the beginningof each update interval). In some embodiments, there is no need tochange the controller because actual values and predicted values of themeasured variable can used by the controller in the same way (i.e., asinputs to a control algorithm) without requiring the controller to knowthat some of its inputs are predicted and not measured.

One advantage of the current invention results from predicting futuremeasured values of the measured variable and sending the controllermultiple predicted values in a single message transmission. This allowsthe controller to maintain the same responsiveness without sacrificingbattery life of the wireless sensor. For example, a building managementsystem may be able to maintain occupant comfort by maintaining acontroller update interval of every minute while only receiving messagesevery four minutes. Better resolution of measurements may be availablewithout transmitting values every controller update interval. Fewertransmissions means less power used by the wireless device. Accordingly,battery life can be extended significantly by employing the systems,methods, and devices of the present disclosure.

Before discussing the FIGURES in detail, it should be noted that theexamples provided in the present disclosure are illustrative only andare not limitations on the scope of invention. For example, a one minutesampling interval is used in several of the examples provided herein tosimplify the presentation of the calculations and algorithms. However,it is contemplated that any of a variety of sampling intervals can beused (e.g., one second, one minute, three minutes, five minutes, etc.)in addition to those specifically described herein without departingfrom the scope of the present disclosure.

Building Management System and HVAC System

Referring now to FIGS. 1-4, an exemplary building management system(BMS) and HVAC system in which the systems and methods of the presentinvention may be implemented are shown, according to an exemplaryembodiment. Referring particularly to FIG. 1, a perspective view of abuilding 10 is shown. Building 10 is served by a BMS. A BMS is, ingeneral, a system of devices configured to control, monitor, and manageequipment in or around a building or building area. A BMS can include,for example, a HVAC system, a security system, a lighting system, a firealerting system, any other system that is capable of managing buildingfunctions or devices, or any combination thereof.

The BMS that serves building 10 includes an HVAC system 100. HVAC system100 may include a plurality of HVAC devices (e.g., heaters, chillers,air handling units, pumps, fans, thermal energy storage, etc.)configured to provide heating, cooling, ventilation, or other servicesfor building 10. For example, HVAC system 100 is shown to include awaterside system 120 and an airside system 130. Waterside system 120 mayprovide a heated or chilled fluid to an air handling unit of airsidesystem 130. Airside system 130 may use the heated or chilled fluid toheat or cool an airflow provided to building 10. An exemplary watersidesystem and airside system which may be used in HVAC system 100 aredescribed in greater detail with reference to FIGS. 2-3.

HVAC system 100 is shown to include a chiller 102, a boiler 104, and arooftop air handling unit (AHU) 106. Waterside system 120 may use boiler104 and chiller 102 to heat or cool a working fluid (e.g., water,glycol, etc.) and may circulate the working fluid to AHU 106. In variousembodiments, the HVAC devices of waterside system 120 may be located inor around building 10 (as shown in FIG. 1) or at an offsite locationsuch as a central plant (e.g., a chiller plant, a steam plant, a heatplant, etc.). The working fluid may be heated in boiler 104 or cooled inchiller 102, depending on whether heating or cooling is required inbuilding 10. Boiler 104 may add heat to the circulated fluid, forexample, by burning a combustible material (e.g., natural gas) or usingan electric heating element. Chiller 102 may place the circulated fluidin a heat exchange relationship with another fluid (e.g., a refrigerant)in a heat exchanger (e.g., an evaporator) to absorb heat from thecirculated fluid. The working fluid from chiller 102 and/or boiler 104may be transported to AHU 106 via piping 108.

AHU 106 may place the working fluid in a heat exchange relationship withan airflow passing through AHU 106 (e.g., via one or more stages ofcooling coils and/or heating coils). The airflow may be, for example,outside air, return air from within building 10, or a combination ofboth. AHU 106 may transfer heat between the airflow and the workingfluid to provide heating or cooling for the airflow. For example, AHU106 may include one or more fans or blowers configured to pass theairflow over or through a heat exchanger containing the working fluid.The working fluid may then return to chiller 102 or boiler 104 viapiping 110.

Airside system 130 may deliver the airflow supplied by AHU 106 (i.e.,the supply airflow) to building 10 via air supply ducts 112 and mayprovide return air from building 10 to AHU 106 via air return ducts 114.In some embodiments, airside system 130 includes multiple variable airvolume (VAV) units 116. For example, airside system 130 is shown toinclude a separate VAV unit 116 on each floor or zone of building 10.VAV units 116 may include dampers or other flow control elements thatcan be operated to control an amount of the supply airflow provided toindividual zones of building 10. In other embodiments, airside system130 delivers the supply airflow into one or more zones of building 10(e.g., via supply ducts 112) without using intermediate VAV units 116 orother flow control elements. AHU 106 may include various sensors (e.g.,temperature sensors, pressure sensors, etc.) configured to measureattributes of the supply airflow. AHU 106 may receive input from sensorslocated within AHU 106 and/or within the building zone and may adjustthe flow rate, temperature, or other attributes of the supply airflowthrough AHU 106 to achieve setpoint conditions for the building zone.

Referring now to FIG. 2, a block diagram of a waterside system 200 isshown, according to an exemplary embodiment. In various embodiments,waterside system 200 may supplement or replace waterside system 120 inHVAC system 100 or may be implemented separate from HVAC system 100.When implemented in HVAC system 100, waterside system 200 may include asubset of the HVAC devices in HVAC system 100 (e.g., boiler 104, chiller102, pumps, valves, etc.) and may operate to supply a heated or chilledfluid to AHU 106. The HVAC devices of waterside system 200 may belocated within building 10 (e.g., as components of waterside system 120)or at an offsite location such as a central plant.

In FIG. 2, waterside system 200 is shown as a central plant having aplurality of subplants 202-212. Subplants 202-212 are shown to include aheater subplant 202, a heat recovery chiller subplant 204, a chillersubplant 206, a cooling tower subplant 208, a hot thermal energy storage(TES) subplant 210, and a cold thermal energy storage (TES) subplant212. Subplants 202-212 consume resources (e.g., water, natural gas,electricity, etc.) from utilities to serve the thermal energy loads(e.g., hot water, cold water, heating, cooling, etc.) of a building orcampus. For example, heater subplant 202 may be configured to heat waterin a hot water loop 214 that circulates the hot water between heatersubplant 202 and building 10. Chiller subplant 206 may be configured tochill water in a cold water loop 216 that circulates the cold waterbetween chiller subplant 206 building 10. Heat recovery chiller subplant204 may be configured to transfer heat from cold water loop 216 to hotwater loop 214 to provide additional heating for the hot water andadditional cooling for the cold water. Condenser water loop 218 mayabsorb heat from the cold water in chiller subplant 206 and reject theabsorbed heat in cooling tower subplant 208 or transfer the absorbedheat to hot water loop 214. Hot TES subplant 210 and cold TES subplant212 may store hot and cold thermal energy, respectively, for subsequentuse.

Hot water loop 214 and cold water loop 216 may deliver the heated and/orchilled water to air handlers located on the rooftop of building 10(e.g., AHU 106) or to individual floors or zones of building 10 (e.g.,VAV units 116). The air handlers push air past heat exchangers (e.g.,heating coils or cooling coils) through which the water flows to provideheating or cooling for the air. The heated or cooled air may bedelivered to individual zones of building 10 to serve the thermal energyloads of building 10. The water then returns to subplants 202-212 toreceive further heating or cooling.

Although subplants 202-212 are shown and described as heating andcooling water for circulation to a building, it is understood that anyother type of working fluid (e.g., glycol, CO2, etc.) may be used inplace of or in addition to water to serve the thermal energy loads. Inother embodiments, subplants 202-212 may provide heating and/or coolingdirectly to the building or campus without requiring an intermediateheat transfer fluid. These and other variations to waterside system 200are within the teachings of the present invention.

Each of subplants 202-212 may include a variety of equipment configuredto facilitate the functions of the subplant. For example, heatersubplant 202 is shown to include a plurality of heating elements 220(e.g., boilers, electric heaters, etc.) configured to add heat to thehot water in hot water loop 214. Heater subplant 202 is also shown toinclude several pumps 222 and 224 configured to circulate the hot waterin hot water loop 214 and to control the flow rate of the hot waterthrough individual heating elements 220. Chiller subplant 206 is shownto include a plurality of chillers 232 configured to remove heat fromthe cold water in cold water loop 216. Chiller subplant 206 is alsoshown to include several pumps 234 and 236 configured to circulate thecold water in cold water loop 216 and to control the flow rate of thecold water through individual chillers 232.

Heat recovery chiller subplant 204 is shown to include a plurality ofheat recovery heat exchangers 226 (e.g., refrigeration circuits)configured to transfer heat from cold water loop 216 to hot water loop214. Heat recovery chiller subplant 204 is also shown to include severalpumps 228 and 230 configured to circulate the hot water and/or coldwater through heat recovery heat exchangers 226 and to control the flowrate of the water through individual heat recovery heat exchangers 226.Cooling tower subplant 208 is shown to include a plurality of coolingtowers 238 configured to remove heat from the condenser water incondenser water loop 218. Cooling tower subplant 208 is also shown toinclude several pumps 240 configured to circulate the condenser water incondenser water loop 218 and to control the flow rate of the condenserwater through individual cooling towers 238.

Hot TES subplant 210 is shown to include a hot TES tank 242 configuredto store the hot water for later use. Hot TES subplant 210 may alsoinclude one or more pumps or valves configured to control the flow rateof the hot water into or out of hot TES tank 242. Cold TES subplant 212is shown to include cold TES tanks 244 configured to store the coldwater for later use. Cold TES subplant 212 may also include one or morepumps or valves configured to control the flow rate of the cold waterinto or out of cold TES tanks 244.

In some embodiments, one or more of the pumps in waterside system 200(e.g., pumps 222, 224, 228, 230, 234, 236, and/or 240) or pipelines inwaterside system 200 include an isolation valve associated therewith.Isolation valves may be integrated with the pumps or positioned upstreamor downstream of the pumps to control the fluid flows in watersidesystem 200. In various embodiments, waterside system 200 may includemore, fewer, or different types of devices and/or subplants based on theparticular configuration of waterside system 200 and the types of loadsserved by waterside system 200.

Referring now to FIG. 3, a block diagram of an airside system 300 isshown, according to an exemplary embodiment. In various embodiments,airside system 300 may supplement or replace airside system 130 in HVACsystem 100 or may be implemented separate from HVAC system 100. Whenimplemented in HVAC system 100, airside system 300 may include a subsetof the HVAC devices in HVAC system 100 (e.g., AHU 106, VAV units 116,ducts 112-114, fans, dampers, etc.) and may be located in or aroundbuilding 10. Airside system 300 may operate to heat or cool an airflowprovided to building 10 using a heated or chilled fluid provided bywaterside system 200.

In FIG. 3, airside system 300 is shown to include an economizer-type airhandling unit (AHU) 302. Economizer-type AHUs vary the amount of outsideair and return air used by the air handling unit for heating or cooling.For example, AHU 302 may receive return air 304 from building zone 306via return air duct 308 and may deliver supply air 310 to building zone306 via supply air duct 312. In some embodiments, AHU 302 is a rooftopunit located on the roof of building 10 (e.g., AHU 106 as shown inFIG. 1) or otherwise positioned to receive both return air 304 andoutside air 314. AHU 302 may be configured to operate exhaust air damper316, mixing damper 318, and outside air damper 320 to control an amountof outside air 314 and return air 304 that combine to form supply air310. Any return air 304 that does not pass through mixing damper 318 maybe exhausted from AHU 302 through exhaust damper 316 as exhaust air 322.

Each of dampers 316-320 may be operated by an actuator. For example,exhaust air damper 316 may be operated by actuator 324, mixing damper318 may be operated by actuator 326, and outside air damper 320 may beoperated by actuator 328. Actuators 324-328 may communicate with an AHUcontroller 330 via a communications link 332. Actuators 324-328 mayreceive control signals from AHU controller 330 and may provide feedbacksignals to AHU controller 330. Feedback signals may include, forexample, an indication of a current actuator or damper position, anamount of torque or force exerted by the actuator, diagnosticinformation (e.g., results of diagnostic tests performed by actuators324-328), status information, commissioning information, configurationsettings, calibration data, and/or other types of information or datathat may be collected, stored, or used by actuators 324-328. AHUcontroller 330 may be an economizer controller configured to use one ormore control algorithms (e.g., state-based algorithms, extremum seekingcontrol (ESC) algorithms, proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, feedback control algorithms, etc.)to control actuators 324-328.

Still referring to FIG. 3, AHU 302 is shown to include a cooling coil334, a heating coil 336, and a fan 338 positioned within supply air duct312. Fan 338 may be configured to force supply air 310 through coolingcoil 334 and/or heating coil 336 and provide supply air 310 to buildingzone 306. AHU controller 330 may communicate with fan 338 viacommunications link 340 to control a flow rate of supply air 310. Insome embodiments, AHU controller 330 controls an amount of heating orcooling applied to supply air 310 by modulating a speed of fan 338.

Cooling coil 334 may receive a chilled fluid from waterside system 200(e.g., from cold water loop 216) via piping 342 and may return thechilled fluid to waterside system 200 via piping 344. Valve 346 may bepositioned along piping 342 or piping 344 to control a flow rate of thechilled fluid through cooling coil 334. In some embodiments, coolingcoil 334 includes multiple stages of cooling coils that can beindependently activated and deactivated (e.g., by AHU controller 330, byBMS controller 366, etc.) to modulate an amount of cooling applied tosupply air 310.

Heating coil 336 may receive a heated fluid from waterside system 200(e.g., from hot water loop 214) via piping 348 and may return the heatedfluid to waterside system 200 via piping 350. Valve 352 may bepositioned along piping 348 or piping 350 to control a flow rate of theheated fluid through heating coil 336. In some embodiments, heating coil336 includes multiple stages of heating coils that can be independentlyactivated and deactivated (e.g., by AHU controller 330, by BMScontroller 366, etc.) to modulate an amount of heating applied to supplyair 310.

Each of valves 346 and 352 may be controlled by an actuator. Forexample, valve 346 may be controlled by actuator 354 and valve 352 maybe controlled by actuator 356. Actuators 354-356 may communicate withAHU controller 330 via communications links 358-360. Actuators 354-356may receive control signals from AHU controller 330 and may providefeedback signals to controller 330. In some embodiments, AHU controller330 receives a measurement of the supply air temperature from atemperature sensor 362 positioned in supply air duct 312 (e.g.,downstream of cooling coil 334 and/or heating coil 336). AHU controller330 may also receive a measurement of the temperature of building zone306 from a temperature sensor 364 located in building zone 306.

In some embodiments, AHU controller 330 operates valves 346 and 352 viaactuators 354-356 to modulate an amount of heating or cooling providedto supply air 310 (e.g., to achieve a setpoint temperature for supplyair 310 or to maintain the temperature of supply air 310 within asetpoint temperature range). The positions of valves 346 and 352 affectthe amount of heating or cooling provided to supply air 310 by coolingcoil 334 or heating coil 336 and may correlate with the amount of energyconsumed to achieve a desired supply air temperature. AHU controller 330may control the temperature of supply air 310 and/or building zone 306by activating or deactivating coils 334-336, adjusting a speed of fan338, or a combination of both.

Still referring to FIG. 3, airside system 300 is shown to include abuilding management system (BMS) controller 366 and a client device 368.BMS controller 366 may include one or more computer systems (e.g.,servers, supervisory controllers, subsystem controllers, etc.) thatserve as system level controllers, application or data servers, headnodes, or master controllers for airside system 300, waterside system200, HVAC system 100, and/or other controllable systems that servebuilding 10. BMS controller 366 may communicate with multiple downstreambuilding systems or subsystems (e.g., HVAC system 100, a securitysystem, a lighting system, waterside system 200, etc.) via acommunications link 370 according to like or disparate protocols (e.g.,LON, BACnet, etc.). In various embodiments, AHU controller 330 and BMScontroller 366 may be separate (as shown in FIG. 3) or integrated. In anintegrated implementation, AHU controller 330 may be a software moduleconfigured for execution by a processor of BMS controller 366.

In some embodiments, AHU controller 330 receives information from BMScontroller 366 (e.g., commands, setpoints, operating boundaries, etc.)and provides information to BMS controller 366 (e.g., temperaturemeasurements, valve or actuator positions, operating statuses,diagnostics, etc.). For example, AHU controller 330 may provide BMScontroller 366 with temperature measurements from temperature sensors362-364, equipment on/off states, equipment operating capacities, and/orany other information that can be used by BMS controller 366 to monitoror control a variable state or condition within building zone 306.

Client device 368 may include one or more human-machine interfaces orclient interfaces (e.g., graphical user interfaces, reportinginterfaces, text-based computer interfaces, client-facing web services,web servers that provide pages to web clients, etc.) for controlling,viewing, or otherwise interacting with HVAC system 100, its subsystems,and/or devices. Client device 368 may be a computer workstation, aclient terminal, a remote or local interface, or any other type of userinterface device. Client device 368 may be a stationary terminal or amobile device. For example, client device 368 may be a desktop computer,a computer server with a user interface, a laptop computer, a tablet, asmartphone, a PDA, or any other type of mobile or non-mobile device.Client device 368 may communicate with BMS controller 366 and/or AHUcontroller 330 via communications link 372.

Referring now to FIG. 4, a block diagram of a building management system(BMS) 400 is shown, according to an exemplary embodiment. BMS 400 may beimplemented in building 10 to automatically monitor and control variousbuilding functions. BMS 400 is shown to include BMS controller 366 and aplurality of building subsystems 420. Building subsystems 420 are shownto include a fire safety system 422, a lift/escalators subsystem 424, abuilding electrical subsystem 426, an information communicationtechnology (ICT) subsystem 428, a security subsystem 430, a HVACsubsystem 432, and a lighting subsystem 434. In various embodiments,building subsystems 420 can include fewer, additional, or alternativesubsystems. For example, building subsystems 420 may also oralternatively include a refrigeration subsystem, an advertising orsignage subsystem, a cooking subsystem, a vending subsystem, a printeror copy service subsystem, or any other type of building subsystem thatuses controllable equipment and/or sensors to monitor or controlbuilding 10. In some embodiments, building subsystems 420 includewaterside system 200 and/or airside system 300, as described withreference to FIGS. 2-3.

Each of building subsystems 420 may include any number of devices,controllers, and connections for completing its individual functions andcontrol activities. HVAC subsystem 432 may include many of the samecomponents as HVAC system 100, as described with reference to FIGS. 1-3.For example, HVAC subsystem 432 may include a chiller, a boiler, anynumber of air handling units, economizers, field controllers,supervisory controllers, actuators, temperature sensors, and otherdevices for controlling the temperature, humidity, airflow, or othervariable conditions within building 10. Lighting subsystem 434 mayinclude any number of light fixtures, ballasts, lighting sensors,dimmers, or other devices configured to controllably adjust the amountof light provided to a building space. Security subsystem 430 mayinclude occupancy sensors, video surveillance cameras, digital videorecorders, video processing servers, intrusion detection devices, accesscontrol devices and servers, or other security-related devices.

Still referring to FIG. 4, BMS controller 366 is shown to include acommunications interface 404 and a BMS interface 402. Interface 404 mayfacilitate communications between BMS controller 366 and externalapplications (e.g., monitoring and reporting applications, enterprisecontrol applications, remote systems and applications, applicationsresiding on client devices, etc.) for allowing user control, monitoring,and adjustment to BMS controller 366 and/or subsystems 420. Interface404 may also facilitate communications between BMS controller 366 andthe client devices. BMS interface 402 may facilitate communicationsbetween BMS controller 366 and building subsystems 420 (e.g., HVAC,lighting, security, lifts, power distribution, business, etc.).

Interfaces 402 and 404 can be or include wired or wirelesscommunications interfaces (e.g., jacks, antennas, transmitters,receivers, transceivers, wire terminals, etc.) for conducting datacommunications with building subsystems 420 or other external systems ordevices. In various embodiments, communications via interfaces 402 and404 may be direct (e.g., local wired or wireless communications) or viaa communications network (e.g., a WAN, the Internet, a cellular network,etc.). For example, interfaces 402 and 404 can include an Ethernet cardand port for sending and receiving data via an Ethernet-basedcommunications link or network. In another example, interfaces 402 and404 can include a WiFi transceiver for communicating via a wirelesscommunications network. In another example, one or both of interfaces402 and 404 may include cellular or mobile phone communicationstransceivers. In one embodiment, communications interface 404 is a powerline communications interface and BMS interface 402 is an Ethernetinterface. In other embodiments, both communications interface 404 andBMS interface 402 are Ethernet interfaces or are the same Ethernetinterface.

BMS controller 366 may communicate with a wireless device 408. In someembodiments, device 408 includes a wireless sensor. For example, device408 may include wireless communications abilities and may be able totransmit measured and/or predicted data values to BMS controller 366.Device 408 may be a wireless standalone sensor that is not part ofanother device. For example, device 408 may be a wireless sensor hiddenin a wall, attached to a light fixture, etc. and may be batteryoperated. In some embodiments, device 408 is integrated with a subsystemof building subsystems 420. For example, device 408 may be a sensorinstalled in a duct of HVAC subsystem 432. Device 408 may contain one ormore of a variety of sensors (e.g., temperature sensors, pressuresensors, etc.) used to monitor building 10.

In some embodiments, device 408 may be a smartphone or tablet. In otherembodiments, device 408 may be a laptop or desktop computer, and may notbe wireless. Wireless device 408 may be any device which is capable ofcommunication with BMS controller 366 through communications interface404, and is not limited to the explicitly enumerated devices. It iscontemplated that wireless device 408 may communicate with buildingsubsystems 420 directly. BMS controller 366 may transmit building datato device 408 for processing or analysis. Building data may include anyrelevant data obtained from a component within the building orpertaining to a portion or subsystem of the building. For example,building data may be data from sensors, status control signals, feedbacksignals from a device, calculated metrics, setpoints, configurationparameters, etc. In some implementations, building data is derived fromdata collected.

Wireless device 408 may transmit control data to BMS controller 366 insome embodiments. Control data may be any data which affects operationof the BMS. In some embodiments, control data may control buildingsubsystems 420 through BMS controller 366. For example, wireless device408 may send a signal with a command to enable intrusion detectiondevices of security subsystem 430. Wireless device 408 may receivebuilding data from BMS controller 366 through communications interface404.

Still referring to FIG. 4, BMS 400 may include a database 406. Database406 may include a history of time series values that are measured orcalculated in BMS 400. For example, database 406 may include one or moremeasured or calculated temperatures (e.g., refrigerant temperatures,cold water supply temperatures, hot water supply temperatures, supplyair temperatures, zone temperatures, etc.), pressures (e.g., evaporatorpressure, condenser pressure, supply air pressure, etc.), flow rates(e.g., cold water flow rates, hot water flow rates, refrigerant flowrates, supply air flow rates, etc.), valve positions, resourceconsumptions (e.g., power consumption, water consumption, electricityconsumption, etc.), control setpoints, model parameters (e.g.,regression model coefficients), or any other time series values thatprovide information about the performance of BMS 400 or a componentthereof.

In some embodiments, the time series values in database 406 are measuredby various sensors of BMS 400. For example, BMS 400 may include one ormore temperature sensors, humidity sensors, enthalpy sensors, pressuresensors, lighting sensors, flow rate sensors, voltage sensors, valveposition sensors, load sensors, resource consumption sensors, and/or anyother type of sensor capable of measuring a variable of interest in BMS400. BMS 400 may use the sensors to measure values of one or more timeseries variables (e.g., environmental variables, control variables,etc.) or parameters that are monitored by BMS 400. In some embodiments,the sensors may be part of device 408. BMS 400 and/or wireless device408 may use the time series values to calculate a value for each of aplurality of times. A history of values may be stored in database 406.In some embodiments, the values in database 406 are measured valuesobserved by sensors and/or device 408 of BMS 400. In other embodiments,the values in database 406 are provided by an external data source(e.g., data from a utility supplier).

BMS controller 366 is shown to include a processing circuit 410including a processor 412 and memory 414. Processing circuit 410 may becommunicably connected to BMS interface 402 and/or communicationsinterface 404 such that processing circuit 410 and the variouscomponents thereof can send and receive data via interfaces 402 and 404.Processor 412 can be implemented as a general purpose processor, anapplication specific integrated circuit (ASIC), one or more fieldprogrammable gate arrays (FPGAs), a group of processing components, orother suitable electronic processing components.

Memory 414 (e.g., memory, memory unit, storage device, etc.) may includeone or more devices (e.g., RAM, ROM, Flash memory, hard disk storage,etc.) for storing data and/or computer code for completing orfacilitating the various processes, layers and modules described in thepresent application. Memory 414 may be or include volatile memory ornon-volatile memory. Memory 414 may include database components, objectcode components, script components, or any other type of informationstructure for supporting the various activities and informationstructures described in the present application. According to anexemplary embodiment, memory 414 is communicably connected to processor412 via processing circuit 410 and includes computer code for executing(e.g., by processing circuit 410 and/or processor 412) one or moreprocesses described herein.

In some embodiments, BMS controller 366 is implemented within a singlecomputer (e.g., one server, one housing, etc.). In various otherembodiments BMS controller 366 may be distributed across multipleservers or computers (e.g., that can exist in distributed locations).For example, BMS controller 366 may be implemented as part of a METASYS®brand building automation system, as sold by Johnson Controls Inc. Inother embodiments, BMS controller 366 may be a component of a remotecomputing system or cloud-based computing system configured to receiveand process data from one or more building management systems. Forexample, BMS controller 366 may be implemented as part of a PANOPTIX®brand building efficiency platform, as sold by Johnson Controls Inc. Inother embodiments, BMS controller 366 may be a component of a subsystemlevel controller (e.g., a HVAC controller), a subplant controller, adevice controller (e.g., AHU controller 330, a chiller controller,etc.), a field controller, a computer workstation, a client device, orany other system or device that receives and processes data.

Still referring to FIG. 4, memory 414 is shown to include a messageparser 416 and a feedback controller 418. Modules 416 and 418 may beconfigured to receive inputs from building subsystems 420, wirelessdevice 408, and other data sources, determine optimal control actionsfor building subsystems 420 based on the inputs, generate controlsignals based on the optimal control actions, and provide the generatedcontrol signals to building subsystems 420. The following paragraphsdescribe some of the general functions performed by each of modules 416and 418 in BMS 400.

Message parser 416 may be configured to parse data received by BMScontroller 366. For example, a message containing multiple data values(e.g., measured values and/or predicted values) may be received by BMScontroller 366. Message parser 416 may be configured to parse themessage and extract the multiple data values. Message parser 416 mayprovide one value at a time to feedback controller 418. Message parser416 may also or alternatively be configured to flag values as measuredor predicted. In yet other embodiments, message parser 416 may provideonly values of a certain type to feedback controller 418. For example,message parser 416 may only provide measured values to feedbackcontroller 418. In some embodiments, message parser 416 may organize ororder data based on the values or flags associated with the values. Forexample, message parser 416 may rearrange the message received by BMScontroller 366 such that measured values appear before predicted values,or vice versa. In some embodiments, message parser 416 can work withfeedback controller 418 to optimize building performance (e.g.,efficiency, energy use, comfort, or safety) based on inputs received atinterface 404 and/or BMS interface 402.

Feedback controller 418 may be configured to leverage measurements tomake decisions about how to manipulate the operation of BMS 400 so thatBMS 400 achieves an expected or desired behavior. For example, feedbackcontroller 418 may be able to receive inputs from message parser 416 ofmeasured and predicted values. In some embodiments, feedback controller418 does not need to know whether the values are measured or predicted.For example, an existing system may be able to interact with wirelessdevice 408 without being significantly altered, or altered at all, byreceiving both measured and predicted values at regular intervalswithout being informed of the type of each value. Feedback controller418 may use the measured and/or predicted values as inputs to a controlalgorithm as if all of the values were measured. This may providesavings to consumers by reducing the amount of reconfiguration andreinstallations needed.

Wireless Device Transmission Timing

Battery life is a critical competitive concern, and it is necessary toinvestigate methods to extend the effective battery life of wirelessdevices. The estimated battery life of wireless devices must be knownwith some reasonable level of confidence in order for sales teams toengage customers, quote wireless product offerings, and estimate batteryreplacement intervals appropriately. A wireless device may include aradio for communication, which may communicate measurements to thecontroller. In many cases, the radio transmissions consume the mostpower during operation. Some device radios have battery life of twoyears or less, which is generally an insufficient amount of time forsome consumers. Extending the battery life may result in a morecompetitive product for customers desiring a longer battery life.

Existing designs involve the radio of a wireless device transmitting onemessage every one or two minutes. In some embodiments, the wirelesscommunications protocol used is ZigBee. In other embodiments,communications protocols may include WiFi, Bluetooth, NFC, etc. Suchfrequent transmissions may drain the battery of a wireless device withintwo years. Previously proposed strategies require changing thecontroller algorithm, and may reduce the integrity of the controller. Insome embodiments, the measured variable may be zone temperature; if thetime between transmissions is extended too much, the comfort of anoccupant of a zone controlled by the controller may be impacted. Forexample, if the time between transmissions is increased to five minutes,an occupant of a room may be forced to endure uncomfortable conditionsfor five minutes before the system acknowledges a change in temperature.In other embodiments, the measured variable is the same as thecontrolled variable. In some embodiments, the controlled variable may betemperature, humidity, etc. The measured variable and controlledvariable are not limited to those specifically enumerated.

The present disclosure describes a system and method which usesmeasurements and past data to predict the next measurement withaccuracy. The radio of the wireless device may transmit a messagecontaining the current measurement and one or more predictedmeasurements. The controller may receive and use the measurementswithout any change to its algorithm. For example, the controller mayinclude a message parser that extracts multiple values from a singlemessage and provides the extracted values as inputs to the controlalgorithm over a period of time (e.g., one value at the beginning ofeach controller update interval). In some embodiments, the wirelessdevice sends a new message to the controller when a measurement is madethat is inconsistent with a previously-predicted measurement (e.g.,different from the previously-predicted measurement by an amountexceeding a threshold, changing at a rate exceeding a threshold, etc.).

Many existing methods require changes to the control algorithm. In someembodiments, the systems and methods described herein allow thecontroller to function in the same way using the same algorithm withminimal, but acceptable, loss of control integrity. An acceptable lossof control integrity may be a predefined margin of error. The sensorside, or portion of the system containing the sensor, may performmultiple measurements (which typically does not consume as much power asmultiple transmissions) and send one message that contains highlyaccurate predictions based on the measurements and past data. In someembodiments, the controller receives a measurement value at regularintervals consistent with those used in previous methods, and thecontrol algorithm used by the controller may not change. Fewertransmissions may result in less power used by the wireless device. Itis expected that battery life can be extended significantly by employingthe systems, methods, and devices of the present disclosure.

Transmission System Architecture

Referring now to FIGS. 5A-5B, systems 500 and 550 for wirelesslytransmitting data values of a measured variable are shown, according totwo exemplary embodiments. The transmitted data values may includemeasured values, predicted values, or any combination thereof. Systems500 and 550 are shown to include wireless device 408 and BMS controller366. Wireless device 408 is shown to include a sensor 502, a low-powermicrocontroller 504, and a wireless radio chip 514.

Sensor 502 may measure a variable of interest and provide measured datavalues to low-power microcontroller 504. Sensor 502 may be a temperaturesensor, humidity sensor, enthalpy sensor, pressure sensor, lightingsensor, flow rate sensor, voltage sensor, valve position sensor, loadsensor, resource consumption sensor, and/or any other type of sensorcapable of measuring a variable of interest in BMS 400. In someembodiments, sensor 502 includes a plurality of sensors, and wirelessdevice 408 may generate multiple messages or generate one message, eachcontaining measurements (both measured and predicted) for multiplemeasured variables. In some embodiments, sensor 502 is a single sensorand wireless device 408 may generate a single message containingmultiple measurements (measured and predicted) containing data for thesingular measured variable.

Sensor 502 may be any battery-operated sensor, such that sensor 502 doesnot need an external power source. In some embodiments, sensor 502receives power from a battery within wireless device 408. In otherembodiments, sensor 502 may use any power source such as chemical,solar, biological cells, etc. In some embodiments, sensor 502 mayutilize an external power source, and may be connected to the powersource via a wired connection. In other embodiments, sensor 502 mayutilize technology such as wireless charging.

Sensor 502 may collect data values continuously at regular intervals.For example, sensor 502 may collect temperature data in a particularzone of a building every minute. In some embodiments, sensor 502 maycollect multiple values for multiple variables at the same time, or atdifferent frequencies. For example, sensor 502 may be a combinationsensor, and may collect air temperature data every minute and localhumidity every five minutes. The length of time between data collectionsby sensor 502 is referred to herein as the measurement period and/or themeasurement interval.

Low-power microcontroller 504 may generate a message containing one ormore values of the measured variable and, in some embodiments, one ormore predicted values of the measured variable. Low-powermicrocontroller 504 may be any controller component capable ofprocessing data. For example, microcontroller 504 may include aprocessing circuit containing a processor capable of receiving,processing, and outputting data. In some embodiments, microcontroller504 may contains memory capable of storing data. In other embodiments,microcontroller 504 may not include a memory. Various embodiments oflow-power microcontroller 504 are described in greater detail below.Low-power microcontroller 504 provides the message to wireless radiochip 514, which wirelessly transmits the message to BMS controller 366.

BMS controller 366 is shown to include a wireless radio chip 516, amessage parser 526, and a feedback controller 418. Wireless radio chip516 receives messages from wireless radio chip 514 of wireless sensor408 and provides the messages to message parser 416. Message parser 416extracts individual data values from the message and provides the datavalues to feedback controller 418. Feedback controller 418 uses the datavalues as inputs to a control algorithm to generate a control output(e.g., a control signal for building equipment that operate to affectthe measured variable). In some embodiments, BMS controller 366 includessome or all of the features described with reference to FIGS. 3-4. Insome embodiments, systems 500 and 550 may include more, fewer, and/ordifferent components than those specifically shown in FIGS. 5A-5B.

Referring particularly to FIG. 5A, system 500 is shown, according to anexemplary embodiment. In system 500, one or more future values of ameasured variable are predicted by wireless device 408, combined into asingle message along with a current value of the measured variable, andtransmitted to BMS controller 366 wirelessly. In system 500, low-powermicrocontroller 504 is shown to include a future value predictor 506, atransmission timer 508, a value comparator 510, and a message generator512. The functions of these components are described in greater detailbelow.

Future value predictor 506 may be a module of microcontroller 504. Insome embodiments, future value predictor 506 may be a memory modulewhich may contain instructions to be executed by a processor ofmicrocontroller 504. Future value predictor 506 may use measured valuesobtained by sensor 502 to predict future values of the measuredvariable. In some embodiments, future value predictor 506 may beimplemented in hardware, as a circuit. In other embodiments, futurevalue predictor 506 may be implemented in software, ascomputer-executable code. Future value predictor 506 may be implementedas any combination of hardware and software. Any module in the presentdisclosure may be implemented as solely hardware, solely software, or acombination of hardware and software.

Future value predictor 506 may use a prediction model to predict one ormore future values of the measured variable with accuracy over aprediction horizon (how far ahead a model predicts the future) orprediction period of a predetermined length. In some embodiments, thechosen model may be developed to predict over a short horizon to ensurefrequent inputs of data values to feedback controller 418. The chosenmodel may be of low order to reduce computational complexity and thusprocessing time and power consumption. In some embodiments, the chosenmodel may be adaptive to handle a variety of starting conditions andsituations. For example, a model may be designed to handle buildingzones with different air volumes.

Future value predictor 506 may use the chosen model to develop amodel-based predictive algorithm of low computational complexity. Insome embodiments, the algorithm may filter out noise in received data.The predictive algorithm may be executed by low-power microcontroller504 to predict future values for input to feedback controller 418. Insome embodiments, future value predictor 506 executes the predictivealgorithm. In some embodiments, the algorithm may be designed to havelow computational complexity to ensure real-time execution usinglow-power microcontroller 504 which may have limited computation andmemory resources. An exemplary model and algorithm are described in moredetail in the discussion of FIGS. 9A and 10A.

Referring still to FIG. 5A, transmission timer 508 may be a module oflow-power microcontroller 504 configured to monitor and control timingof wireless transmissions from device 408. In some embodiments,transmission timer 508 monitors and controls timing of wirelesstransmissions to BMS controller 366. Transmission timer 508 may beconfigured to identify or determine a transmission period and/ortransmission interval for wireless device 408 based on the originaltransmission interval used by feedback controller 418. As definedherein, the transmission interval may be the time that elapses betweentransmissions from wireless device 408. In some embodiments, thetransmission interval is longer than the controller update interval usedby feedback controller 418. For example, if the controller updateinterval used by feedback controller 418 is one minute, the transmissioninterval may be four minutes. The controller update interval may be theperiod of time between iterations of the control algorithm used byfeedback controller 418. At the beginning of each controller updateinterval, feedback controller 418 may be provided an updated inputvalue. In other embodiments, the transmission interval is equal to thecontroller update used by feedback controller 418.

The transmission interval may be determined by wireless device 408 toprovide a compromise between power savings and accuracy of theprediction values output by device 408. In some embodiments, thetransmission interval is determined by transmission timer 508, and thepredicted values are determined by future value predictor 506.Transmission timer 508 may adjust a transmission interval of wirelessdevice 408 based on instructions from value comparator 510. For example,the regular transmission interval may be shortened if value comparator510 determines that a predicted value for a given time is significantlydifferent than an actual measured value for the given time and/or if themeasured value has changed at a rate exceeding a predeterminedthreshold.

The controller update interval may be shorter than the transmissioninterval such that multiple controller update intervals occur within asingle transmission interval. In other embodiments, the controllerupdate interval may be the same as the transmission interval. In someembodiments, the controller update interval may be no longer thanone-tenth of the time constant of the controlled system. In otherembodiments, the controller update interval may be any length of time(e.g., one-half, two-thirds, two times the time constant, etc.) of thecontrolled system. The time constant of the controlled space maycharacterize the frequency response of the system. For example, the timeconstant of a system may be 20 minutes; the controller update intervalmay be one-twentieth the time constant, or one minute. In someembodiments, the update interval is determined based on user or occupantpreference. For example, a user may prefer for the controller updateinterval to be shorter to keep up with constantly changing conditionsand maintain occupant comfort. A user may set the controller updateinterval to be longer to conserve battery life of wireless device 408which may need to transmit more frequently with a shorter updateinterval.

Referring still to FIG. 5A, low-power microcontroller 504 is shown toinclude value comparator 510. Value comparator 510 may be a module ofmicrocontroller 504 configured to compare values of the measuredvariable. In some embodiments, value comparator 510 may compare apreviously-predicted value for a particular time to an actual measuredvalue at that time and make an operating decision based on the result ofthe comparison. For example, value comparator 510 may compare apreviously-predicted value to an actual measured value to determine adifference between the predicted and measured values. In someembodiments, value comparator 510 compares multiple measured values atdifferent times to determine a rate of change. The difference and/orrate of change may be used to make operating decisions. For example, ifthe difference and/or rate of change is larger than a predeterminedthreshold, wireless device 408 may send a new measured value to BMScontroller 366 before the beginning of the next transmission interval.

In some embodiments, value comparator 510 may communicate withtransmission timer 508 to transmit a measured value before the beginningof the next transmission interval to BMS controller 366 based on acalculated rate of change. For example, if value comparator 510determines that the rate of change of the zone temperature is largerthan a predetermined threshold, value comparator 510 may communicatewith transmission timer 508 to send a measured value before thebeginning of the next transmission interval. In some embodiments,wireless device 408 may make control decisions based on the differencebetween a predicted value at a time and its measured value at that time.For example, value comparator 510 may calculate the difference betweenthe predicted value and the measured value of a flow rate, and upondetermining that the difference is larger than a predetermined thresholdvalue, may communicate with transmission timer 508 to send a measuredvalue to BMS controller 366 before the beginning of the nexttransmission interval.

Still referring to FIG. 5A, low-power microcontroller 504 is shown toinclude message generator 512. Message generator 512 may be a module ofmicrocontroller 504 configured to generate a message for transmittingfrom device 408. In some embodiments, message generator 512 may generatea message which contains one or more measured values and one or morepredicted values of a measured variable for transmitting from device 408to BMS controller 366. In some embodiments, messages include indicatorsfor whether a value is a measured or predicted value. For example,message generator 512 may generate a message including one measuredvalue and three predicted values. The predicted values may be marked bya flag or attribute to indicate to BMS controller 366 that the valuesare predicted values. In other embodiments, message generator 512 maynot mark different values, and may not inform BMS controller 366 whethera value is measured or predicted.

In some embodiments, message generator 512 generates messages with onlymeasured values, only predicted values, or a combination of measured andpredicted values. Message generator 512 may generate messages such thatincluded values are sequential. In some embodiments, message generator512 may generate messages with indicators for the order in which thevalues should be read. For example, a message may be a data packetcontaining one measured value and multiple predicted values, each with aheader indicating its status (measured or predicted) and its temporalorder. In other embodiments, message generator 512 may generate messageswith the values in the order in which each value is to be read and/orapplied as an input to feedback controller 418.

Message generator 512 may generate messages such that no changes need tobe made to the control algorithm of BMS controller 366 in order toreceive the messages. In some embodiments, message generator 512 maygenerate messages such that few changes need to be made to the controlalgorithm of BMS controller 366 in order to receive the messages.Message generator 512 may generate messages with commands or controldata for transmitting to BMS controller 366.

Still referring to FIG. 5A, wireless device 408 and BMS controller 366are shown to include wireless radio chips 514 and 516. In someembodiments, the wireless communications protocol used is ZigBee. Othercommunications protocols include WiFi, Bluetooth, NFC, etc. In someembodiments, other communications interfaces and components may beincluded, such as a wired connection. Wireless radio chips 514 and 516may contain transceivers capable of transmitting and receiving datathrough an antenna. Wireless radio chips 514 and 516 may be differentchips and may use different hardware while using the same wirelesscommunications protocol. Wireless radio chips 514 and 516 may operateusing any frequency interface, such as RF. Chips 514 and 516 may usefrequencies outside of the RF range, and may not be radio chips. In someembodiments, chips 514 and 516 may communicate using other frequencyranges, such as IR. Chips 514 and 516 may utilize any communicationsinterface, and are not limited to those specifically enumerated.

Still referring to FIG. 5A, BMS controller 366 is shown to includemessage parser 416 and feedback controller 418. These components may bethe same or similar as previously described with reference to FIG. 4.For example, message parser 416 may parse messages generated by messagegenerator 512 and received by BMS controller 366. In some embodiments,messages are received directly by message parser 416. Message parser 416may communicate with feedback controller 418. In some embodiments,message parser 416 parses a message, extracts a plurality of data valuesfrom the message, and provides the data values to feedback controller418 at regular intervals.

Message parser 416 may receive messages at the transmission interval,which may be longer than the controller update interval. Message parser416 may parse the messages to extract a data value for each controllerupdate interval within the transmission interval. For example, messageparser 416 may receive a message with six values: one measured value andfive predicted values. Message parser 416 may extract the six valuesfrom the message and provide one of the values to feedback controller418 at the beginning of each controller update interval. Feedbackcontroller 418 may receive inputs from message parser 416 to makecontrol decisions for BMS controller 366. In some embodiments, feedbackcontroller 418 generates control data and provides the control data BMSsubsystems 420 (e.g., directly or via BMS interface 404).

Referring now to FIG. 5B, system 550 is shown, according to an exemplaryembodiment. System 500 is shown to include many of the same componentsas system 500 of FIG. 5A. Reused reference numbers indicate similarcomponents. In system 550, future values of the measured variable arepredicted by BMS controller 366 and therefore future value predictor 506may be included in BMS controller 366 rather than wireless device 408.Wireless device 408 is shown to include, in the exemplary embodiment ofFIG. 5B, transmission timer 508 and message generator 512. Messagegenerator 512 may generate messages containing one or more values of themeasured variable. In some embodiments, the messages include a singlevalue of the measured variable. In other embodiments, the messagesinclude multiple values of the measured variable. Wireless radio chip514 may transmit the messages to BMS controller 366 at the transmissioninterval.

BMS controller 366 is shown to include message parser 416, future valuepredictor 506, and feedback controller 418. In some embodiments, messageparser 416 receives messages from wireless device 408 and parsesmessages into values for inputting to feedback controller 418. Thevalues may pass through future value predictor 506 before being input tofeedback controller 418. Future value predictor 506 may predict values,using the values parsed out by message parser 416, for the measuredvariable to pass to feedback controller 418.

In some embodiments, messages received by message parser 416 containmultiple measured values (i.e., past values of the measured variable)which are passed to future value predictor 506, and future valuepredictor 506 may predict values in the future using the multiplemeasured values. In other embodiments, messages received by messageparser 416 contain one measured value which is passed to future valuepredictor 506, and future value predictor 506 may predict values in thefuture using the single measured value. Future value predictor 506 maypredict values one at a time for inputting to feedback controller 418.In some embodiments, future value predictor 506 may predict values eachcontrol interval within a horizon of a predetermined duration. In someembodiments, the prediction horizon begins at the current time and endsat the control interval before a new transmission is scheduled to bereceived. For example, if the transmission interval is every fourminutes, the prediction horizon may be three minutes. This allows futurevalue predictor 506 to predict future values for each control intervalfor which a value of the measured variable is not received from wirelessdevice 408.

Processes for Predicting Future Measurements

A wireless radio chip in a wireless device may use significantly lesspower when receiving data from a low-power microcontroller than it doeswhen it is transmitting a message to a feedback controller. Multiplevalues may be sent to a controller in a single message each time thewireless device transmits, reducing the number of transmissions needed.By incorporating advanced algorithms for predicting future valuesaccurately, the proposed systems and methods of the present disclosuremay increase a transmission interval and conserve power.

In some embodiments, a message sent to a feedback controller may containthe current measured value and multiple predicted values of a measuredvariable. The predicted values may be based on multiple measurements andpast values stored in memory. In some embodiments, one message is senteach transmission interval. If a change of value occurs, it may triggera new message to be sent to the controller with updated values. Thecontroller update interval and the wireless device transmission intervalmay not be equal in some implementations. Instead, a message parser onthe controller side may provide the feedback controller with one valuefrom the message each update interval. In some embodiments, there is noneed to change the implementation or algorithm of the controller—actualvalues and predicted values may be formatted and handled the same withno indicated difference between the two.

One advantage of the proposed method results from providing futurevalues to the feedback controller. A system may be able to maintain thesame responsiveness without sacrificing battery life. For example, abuilding management system may be able to maintain occupant comfort bymaintaining a controller update interval of every minute while onlyreceiving messages every four minutes. Better resolution of measurementsmay be available without transmitting values every controller updateinterval. There are two limiting factors to the length of thetransmission interval: the accuracy of the algorithm, which determinesthe prediction horizon; and the power consumed by the wireless device intaking measurements and running the algorithm. Fewer transmissions meansless power used by the wireless device. It is expected that battery lifecan be extended significantly by employing the systems, methods, anddevices of the present disclosure.

Referring now to FIG. 6, a graph 600 illustrating an existing wirelesstransmission technique is shown, according to an exemplary embodiment.X-axis 602 is shown to be time. In some embodiments, the units areminutes and the transmissions 606 are shown to occur every minute.Y-axis 604 is shown to be the value of the measured variable. The datacontained in each transmission may be measured values 608 of themeasured variable. Graph 600 shows the interval at which a wirelessdevice may transmit messages to a controller. In some embodiments, onemeasured value is received per transmission and the transmissioninterval is the same as the controller update interval (i.e., onemeasurement and transmission per minute). Many existing methods use aone-to-one ratio of transmissions and measured values, consuming largeamounts of power in transmission.

Referring now to FIG. 7, a process 700 illustrating the existingwireless transmission technique of FIG. 6 is shown, according to anexemplary embodiment. Process 700 begins with step 702, in which thechosen building variable, or measured variable, is measured. Step 702may be performed by a wireless device which may contain a sensor. Insome embodiments, the sensor may be wireless or it may require a wiredconnection. The process continues with step 704, in which a message isgenerated containing a single measurement of the building variable. Themessage may be generated by the wireless device, and in someembodiments, may be generated by a microcontroller or module of themicrocontroller in the wireless device.

The message may then be transmitted in step 706. In some embodiments,the message is transmitted wirelessly to a controller. The controllermay receive the message in step 708. In some embodiments, the controllermay include a microcontroller which may parse the message in step 710 toobtain a value which may be input to a controller. The controller mayinclude a feedback controller which makes control decisions based oninput received from the wireless device. Process 700 continues with step712, in which the value obtained in step 710 is input to the feedbackcontroller. In many existing methods, process 700 repeats by going backto step 702. Transmissions may commonly be made with one value,requiring one transmission per controller update interval.

Referring now to FIG. 8, a graph 800 illustrating a new wirelesstransmission technique is shown, according to an exemplary embodiment.X-axis 802 is shown to be time. In some embodiments, the units areminutes and the transmissions 806 are shown to occur at regularintervals of four minutes. In other embodiments, transmissions 806 mayoccur at any regular interval, such as every two, seven, fifteen, etc.,minutes. The time that elapses between transmissions is referred toherein as transmission interval 808. In some embodiments, transmissions806 may occur at irregular intervals.

Still referring to FIG. 8, y-axis 804 is shown to be the value of themeasured variable. The data contained in each transmission may includeone or more measured values 810 of the measured variable and one or morepredicted values 812. Each of predicted values 812 is shown to includebars which may indicate a confidence interval or uncertainty for eachvalue. The level of confidence may be chosen by a user, or may beselected automatically by the system based on user inputs andpreferences for reactiveness to changes in the system. For example, auser may desire a lower confidence level of 80% for a system controllingthe temperature in an unoccupied zone of a building, while he may choosea confidence level of 95% for a system controlling the humidity in alaboratory setting. The confidence level may affect the computationalcomplexity of the selected algorithm used to predict predicted values812, and decisions regarding the confidence level may be made by a useror the system based on the amount of power or computational timerequired to deliver such a confidence level.

In some embodiments, the bars may represent a margin of error which isacceptable. In other embodiments, the bars may represent upper and lowerthresholds for values. For example, an alarm may be activated if it isdetermined that a later measured value 810 at the time of a predictedfuture value 812 is outside of one of the threshold values. A measuredvalue 810 may be sent to the feedback controller prior to the beginningof the next transmission interval if a later measured value 810 at thetime of a predicted future value 812 is outside of one of the thresholdvalues.

Still referring to FIG. 8, in this exemplary embodiment, graph 800 showstransmissions 806 occurring at time 1 and time 5. Graph 800 showsmeasured values 810 at time 1 and time 5 and predicted values 812 attimes 2-4 and times 6-7. The transmission interval has been extendedfrom the one minute interval shown in FIG. 6 to four minutes (i.e., onetransmission every four minutes). At time 1, a message may be sentcontaining the actual measured value 810 and three predicted futurevalues 812. The values at times 2, 3, and 4 may be estimated and may beable to tolerate a slight variation from the actual value, indicated bythe bars indicating the confidence interval.

A sensor may measure the measured variable at the beginning of eachmeasurement interval (e.g., once per minute) and run the predictionalgorithm. A message may then be sent once per transmission interval(e.g., every four minutes). Each message may include a measured valueand one or more predicted values of the variable of interest. Forexample, the message generated at time 1 may include the actual valuemeasured at time 1 as well as predicted values for times 2-4. Themessage generated at time 5 may include the actual value measured attime 5 as well as predicted values for times 6-8. The interval between ameasured value and the most distant predicted value is shown as theprediction interval. In other words, the prediction interval defines howfar in advance predictions are made. For example, graph 800 is shown toinclude a prediction interval of three minutes, extending from time 1 totime 4.

A new message may be generated and sent at the beginning of eachtransmission interval. However, it is contemplated that a new messagemay also be generated and sent if a change in value is detected. Achange in value may include, for example a substantial differencebetween a measured and predicted value for a given time (e.g., adifference in excess of a threshold), a measured value that lies outsidea threshold for a predicted value, a rate of change that exceeds athreshold, or other changes in a measured value relative to apreviously-predicted or previously-measured value.

A model-based predictive sensing algorithm for feedback control isdescribed in the present disclosure. In some embodiments, no change ismade to the controller update interval of the feedback controller. Thefeedback controller may be executed as if it were to receive ameasurement at each time step required for acceptable controlperformance. The transmission intervals between the wireless device andthe controller may be increased to be a multiple of the controllerupdate rate of the controller (e.g., one transmission per fourcontroller update intervals of the controller). To provide the feedbackcontroller with measurement values between transmissions, the wirelessdevice may also transmit predicted values of the future measurements tothe feedback controller.

Referring now to FIGS. 9A-9B, a system 900 and process 950 for theproposed wireless transmission timing method which may be performed bythe system of FIG. 4 are shown, according to an exemplary embodiment.System 900 and process 950 may include step 902, in which one or morevalues of a building variable are measured. In some embodiments, step902 may be performed by wireless device 408. Referring to FIGS. 5A and9A-9B, step 902 may be performed by sensor 502. In some embodiments,step 902 is executed by another component of wireless device 408 whichmay not be shown. The measurement interval (i.e., the time betweenconsecutive measurements) may be shorter than the transmission interval(i.e., the time between consecutive transmissions) or equal to thetransmission interval. For example, samples of the measured variable maybe obtained every minute, whereas a transmission may occur every fourminutes.

System 900 and process 950 may continue with step 904 in which futurevalues of the building variable (or measured variable) are predicted. Insome embodiments, step 904 is performed by wireless device 408. Step 904may be performed by low-power microcontroller 504, or a component oflow-power microcontroller 504. In some embodiments, step 904 isperformed by future value predictor 506 of wireless device 408. Thealgorithm for predicting the future value may use any prediction method.In some embodiments, filters, such as a Kalman filter may be used topredict future values of the measured variable. In other embodiments,deterministic and stochastic models are used. Autoregressive models maybe used to predict future values of the measured variable. Exemplarymodels and algorithms are described later in the present disclosure.

In step 906, wireless device 408 may determine whether to generate amessage. In some embodiments, wireless device 408 decides to generate anew message when a change in value occurs (e.g., when an error between apredicted value and a later-measured value for the same time exceeds athreshold, when a rate of change of the measured values a threshold,etc.). In some embodiments, the generation of a new message leads to thetransmission of the new message. The transmission of a new message maybegin a new transmission interval. For example, a transmission intervalmay be four minutes long, and may span time 1 to time 5. If a change ofvalue is detected at time 3, a new transmission interval may begin attime 3 and span time 3 to time 7. In some embodiments, if a user makes achange to the system, a new message may be generated. For example, if auser changes a setpoint or a setting of the system, a new message may begenerated for transmittal to the feedback controller.

The time constant of a system may change, making predictions inaccurate.For example, the frequency response of a temperature control system maychange due to a change in occupancy of a particular zone. In someembodiments, the system retunes and/or redefines the predictionalgorithm if an error commonly occurs or if an error occurs too often(e.g., at a rate in excess of a predetermined limit or threshold). Inother embodiments, the system retunes and/or redefines the predictionalgorithm if new messages are sent too often or corrections need to bemade too often. For example, if a new message is being sent twice intwenty controller update intervals, the system may retune the predictionalgorithm. Retuning can occur periodically and automatically. Forexample, retuning of the algorithm may occur every five (or any othernumber) of controller update intervals. In some embodiments, retuningoccurs based on conditions such as a frequency of errors. For example,if an error is detected three times in ten controller update intervals,retuning of the prediction algorithm may occur.

System 900 and process 950 may include step 908, in which a message isgenerated. Referring to FIGS. 5A and 9A-9B, a message 910 may begenerated containing measured value(s) 912 and predicted value(s) 914.In various embodiments, measured value 912 may include a single measuredvalue (as shown in FIG. 9A) or multiple measured values. Predictedvalues 914 may be a single value or multiple predicted values. In someembodiments, message 910 may contain only measured value 912 and nopredicted values 914. In other embodiments, message 910 only containspredicted values 914. Message 910 may contain any combination ofmeasured value 912 and predicted values 914.

Message 910 may be generated such that the existing controller may beused without making changes to the control algorithm. For example,message 910 may be in a format that may be read by message parser 416.In some embodiments, message 910 is generated such that changes may needto be made to the control algorithm. Message 910 may be generated in aformat which may not be directly input to the controller. In someembodiments, the controller is BMS controller 366. In other embodiments,the controller is a component of BMS controller 366, such as feedbackcontroller 418. Message 910 may be parsed by message parser 416 suchthat the processed values may be input directly to feedback controller418.

Referring still to FIGS. 9A-9B, system 900 and process 950 may includestep 916 in which message 910 is transmitted to the controller. In someembodiments, the controller is BMS controller 366 or feedback controller418. Step 916 may occur through the use of wireless radio chips 514 and516 of FIG. 5A. In some embodiments, transmission step 916 occurswirelessly over wireless communications protocols such as ZigBee, WiFi,Bluetooth, NFC, etc. In other embodiments, transmission step 916 mayrequire a physical connection or a wired connection.

System 900 and process 950 may further include step 918, in whichmessage 910 is received at the controller. In some embodiments, message910 is transmitted from wireless device 408 and is received by BMScontroller 366. Message 910 may be transmitted through wireless radiochip 514 and received wireless radio chip 516. In some embodiments,message 910 may be received by a component of BMS controller 366.

System 900 and process 950 may further include step 920, in whichmessage 910 is parsed to obtain values. In some embodiments, step 920 isperformed by BMS controller 366. In other embodiments, step 920 isperformed by a component of BMS controller 366 such as message parser416. Message parser 416 may parse message 910 to separate each value forsequential input to feedback controller 418. For example, message parser416 may separate measured value 912 for input to BMS controller 366,then each of predicted values 914. In some embodiments, message 910contains indicators for the order in which the values are to be input tofeedback controller 418. In other embodiments, message parser 416 mayinput values to feedback controller 418 in the order the values arearranged in message 910.

Still referring to FIGS. 9A-9B, system 900 and process 950 may includestep 922, in which the values parsed in step 920 may be periodically andsequentially input to a feedback controller. In some embodiments, thefeedback controller is feedback controller 418. Values may be measuredvalue 912 and/or predicted values 914. In some embodiments, the valuesare marked by indicators with whether each value is measured orpredicted. For example, a value received by feedback controller 418 maybe flagged as a predicted value, and may elicit additional processing inthe control algorithm. In other embodiments, the values are not markedby indicators, and feedback controller 418 may not be aware of whichvalues are measured and which are predicted. Feedback controller 418 maynot be configured to handle predicted values, and in some embodiments,may not be configured to identify extraneous data that is not the valueneeded for the control algorithm.

Referring now to FIG. 9B, process 950 is shown to include steps 952 and954, which may be embodiments of step 906 of FIG. 9A. Repeated figurenumbers indicate similar items or steps. Process 950 may proceed fromstep 904 to step 952. In step 952, wireless device 408 may determinewhether the transmission interval has elapsed. In some embodiments, ifthe time to the next transmission has elapsed, process 950 may continuewith step 908. If the time to the next transmission has not elapsed,wireless device 408 may determine whether a change of value hasoccurred. In some embodiments, if a change of value has occurred,process 950 may continue with step 908. If a change of value has notoccurred, process 950 may continue with making another measurement ofmeasured or building variable in step 902.

In some embodiments, steps 952 and 954 are transposed. In otherembodiments, steps 952 and 954 are performed in parallel. Process 950may evaluate the outcomes of steps 952 and 954 as an OR statement, andin some embodiments, may use a logic gate to determine whether tocontinue. Steps 952 and 954 may be performed, in some embodiments, bycomponents of BMS controller 366. For example, step 952, in which adetermination is made as to whether the time to the next transmissionhas elapsed, may be performed by transmission timer 508. Step 954, inwhich a determination is made as to whether a change of value hasoccurred, may be performed by value comparator 510. In some embodiments,a separate processor or module of low-power microcontroller 504 mayperform steps 952 and 954. In other embodiments, a separate component ofwireless device 408 may perform steps 952 and 954.

Referring still to FIGS. 9A-9B, an exemplary embodiment may now bepresented. The following example is meant to illustrate the systems andmethods of the present disclosure and is not meant to be restrictive.Let T_(s,ctrl) denote the execution rate (e.g., sampling time) of thefeedback controller and T_(s,comm) be the transmission interval at whichthe sensor sends the current measurement along with N_(pred)≥0 predictedfuture measurements, where:

$N_{pred} = {\frac{T_{s,{ctrl}}}{T_{s,{comm}}} - 1.}$

In a conventional wired feedback control loop, or under ideal samplingof a wireless feedback control loop, T_(s,ctrl)=T_(s,comm)=:T_(s) whereT_(s) is dictated by conventional control performance requirements. Insome embodiments, the case in which T_(s,ctrl)=T_(s,comm), whether it bea feedback control loop utilizing a wired or wireless sensor, will bereferred to as ideal sampling in what follows. T_(s,ctrl) may beselected according to conventional control performance requirements(e.g., T_(s,ctrl) may be one-tenth of the time-constant), and T_(s,comm)(or N_(pred)) may be limited by the accuracy of the prediction.

The purpose of the model may be to predict the next N_(pred) futuremeasured variable values of a closed-loop system given a measurement ofthe system at the current time. In an exemplary embodiment, the zonetemperature is controlled, and the predictive model may be used tocompute predictions of the closed-loop temperature measurements atfuture time steps given the current temperature measurement. In someembodiments, identifying a model for use as the predictive model differsfrom traditional system identification. The goal may be to identify amodel that can be used to predict closed-loop measurements as opposed tothe goal being to identify a model capable of predicting the response ofan output to a given input (i.e., open-loop model identification). Withrespect to the model requirements, the model should be capable ofpredicting the future measured variable values with sufficient accuracyover a short prediction horizon. In some embodiments, a user may desirethat the computation required to complete the prediction must berelatively small. The length of the horizon and the computationalcomplexity may be chosen by a user, or may be selected or calculatedautomatically by the system.

A first-order discrete-time model may be chosen as the predictive model.The predictive model may be a one-parameter model of the form of:{tilde over (y)}(k+1)=a{tilde over (y)}(k)+bwhere the index k represents the kth time step, {tilde over (y)}(k) isthe predicted measurement at time step k, a represents the modelparameter, and b is the bias term of the model. The bias term may beconsidered an additional parameter depending on the availablecommunication between the wireless sensor device and the feedbackcontroller, which is discussed further below. The chosen model may beconsidered to be a first-order autoregressive (AR) model. Higher-orderautoregressive models may also be considered to potentially improve thepredictions.

The model parameter may be fit using an ordinary least-squares parameterfit with closed-loop measurement data under ideal sampling conditions(T_(s,ctrl)=T_(s,comm)). In some embodiments, two model fittingprocedures may be considered. The first methodology may use a data-basedapproach to fit the model parameter, a and may use the fitted parameteralong with the control loop setpoint to fit the bias term. The secondmethodology may use a data-based approach to fit both the modelparameter, a, and the bias term, b. In both cases, the data-basedparameter fitting may be completed using ordinary least-squares (i.e.,solving the normal equations). Nevertheless, recursive methods couldalso be considered to fit these parameters.

The following paragraphs describe two exemplary model updatemethodologies using vector and matrix calculations. However, it shouldbe noted that these update procedures are provided for illustrativepurposes only and are not meant to restrict the scope of the presentdisclosure.

Exemplary Model Update Procedure I

Synchronously sampled data of the temperature measurements under idealsampling may be collected over a time horizon of N_(data) time steps.The measurement data vector or training data may be defined as follows:Y _(meas) =[y _(meas)(k−N _(data)),y _(meas)(k−N _(data)+1), . . . ,y_(meas)(k)]^(T)where N_(data) is the total number of total measurements. LetY_(meas,1:N) _(data) ⁻¹ be the first N_(data)−1 elements of Y_(meas) andlet Y_(meas,2:N) _(data) be last N_(data)−1 elements of Y_(meas). LetY ₁ =Y _(meas,1:N) _(data) ⁻¹ −y _(sp)Y ₂ =Y _(meas,2:N) _(data) −y _(sp)where y_(sp) denotes the setpoint of the control loop (assuming this isavailable to the wireless sensor device). The model parameter a may befit by solving the following least-squares problem:

$\min\limits_{a}{{{{\overset{\_}{Y}}_{1}a} - {\overset{\_}{Y}}_{2}}}_{2}^{2}$which has an analytical solution given by:

$a = {\frac{{\overset{\_}{Y}}_{1}^{T}{\overset{\_}{Y}}_{2}}{{\overset{\_}{Y}}_{1}^{T}{\overset{\_}{Y}}_{1}}.}$

When Y ₁ ^(T) Y ₁ is small (i.e., approximately zero), which occurs whenthe training data is sampled when the measured variable is maintainednear the setpoint, the model parameter is not updated. Instead, theprevious value of the parameter may be used to prevent numericaldivision by a small number. In practice, this may pose few limitationson the algorithm, as the model parameter a captures the dynamicbehavior. Thus, it may not be appropriate to update this parameter withdata sampled while the system is at steady-state.

Continuing with the exemplary embodiment of the prediction algorithm,the bias term may be computed from a and y_(sp) as followed:b=(1−a)y _(sp).Using the above equation to update the bias term ensures that y_(sp) isa steady-state value of the model and may prevent steady-stateplant-model mismatch.Exemplary Model Update Procedure II

In the case that the setpoint is unavailable to the wireless sensordevice (e.g., the setpoint is set in the controller and cannot becommunicated to the wireless sensor device) or for cases where it may bepreferential to update the bias term using a data-based approach, thealgorithm may be readily modified to compute the bias term using thedata. Let A:=[Y ₁1_(N) _(data) ⁻¹] where 1_(N) _(data) ⁻¹ is the onevector with N_(data)−1 elements. The modified least-squares problem maybe given by:

$\min\limits_{a,b}{{{A\begin{bmatrix}a \\b\end{bmatrix}} - {\overset{\_}{Y}}_{2}}}_{2}^{2}$with a solution given by:

$\begin{bmatrix}a \\b\end{bmatrix} = {\left( {A^{T}A} \right)^{- 1}A^{T}{{\overset{\_}{Y}}_{2}.}}$

It may be straightforward to show that the 2×2 matrix A^(T)A is(theoretically) invertible and positive definite when Y ₁≠c1_(N) _(data)⁻¹ for any scalar c. As before, when Y ₁≈c1_(N) _(data−1) for somescalar c, the data may have been sampled while the system/plant is atsteady-state. The model update may be rejected and the previous modelparameters may be retained. In some embodiments, due to the availabilityof the minimum and maximum setpoint, the bias term may be verify againstthe minimum and maximum setpoint.

When using the training data to update the bias term, the bias term mayneed to be updated every time the setpoint is updated, otherwise themodel may provide biased predictions to the controller. This may lead tooffset in the response of the system or poor closed-loop performance.Additionally, using the method described above to update the bias termmay require that the control loop operate under ideal sampling everytime the setpoint is changed. Thus, this method may not be desirable forapplications with frequent setpoint changes.

After the model parameter a is computed, a verification step may beperformed. For a∈(−1,0), the training data exhibits high-frequencyoscillation about the setpoint with a period of oscillation about equalto the sampling time of the controller. While it is possible that such acase could happen in practice, this may represent a control loop with apoorly tuned controller (either the gain is too large causing thecontroller to compute aggressive control actions or the sampling time istoo large). For a computed parameter of a=±1, the model may not predicta decay in the measured output to the setpoint, and for a=0, the modelmay predict a constant value equal to the bias term. Thus, a desirablerange for the parameter a may be the open interval (0,1), and if a isoutside the interval, a may be manually set to be in the range or newtraining data may be collected to re-compute the model parameter. Insome embodiments, due to the fact that the predictions may be used tocompute control actions by the controller, the model can be made moreconservative by decreasing the model parameter, a, which ensures that asmaller error will be provided to the controller relative to using thecomputed a.

In some embodiments, main components of the predictive algorithm includea wireless device and a controller. Controller 366 may send a wake-upsignal to wireless device 408 and sensor 502 may measure the output. Ananalog to digital conversion may convert the analog signal of sensor 502to a digital signal. The digital measurement signal may be sent to aKalman filter/predictor to filter the measurement, which leverages thepredictive model described in the previous subsection. The next steps ofthe predictive algorithm may depend on the operation mode of wirelessdevice 408.

Continuing with the exemplary embodiment of the systems and methods ofFIGS. 9A-9B, wireless device 408 may have two operation modes: trainingmode and predictive mode. In training mode, wireless device 408 mayprovide measurements to controller 366 under ideal sampling (i.e.,communication occurs at a rate of T_(s,ctrl)). The filtered measurementmay be sent to controller 366 and may be stored in a memory of wirelessdevice 408. At the end of the training mode, a model update may betriggered using the stored data as the training data.

In the predictive mode, the filtered measurement from the Kalman filtermay be used to initialize a multistep Kalman predictor. The Kalmanpredictor, leveraging the predictive model, may compute predictions ofthe future values at the next N_(pred) time steps. The filteredmeasurement for the current time and the predicted values may be sent tothe controller. At the next N_(pred) time steps, no wake-up signal maybe sent to wireless device 408. Instead, controller 366 may compute acontrol input on the basis of the predicted values. At each executionstep of controller 366, it may be desirable (but not required) forwireless device 408 to receive measurements to update the Kalman filter.

The Kalman filter and multistep predictor is described in detail. In anexemplary embodiment, a state-space realization of the system may be asfollows:x(k+1)=ax(k)+w(k)y(k)=x(k)+v(k)where x(k) denotes the state at time step k, w(k) is process noise, andy(k) is the output measurement, which may be corrupted by somemeasurement noise, denoted by v(k). For purposes of the Kalman filter,w(k) and v(k) may be assumed to be white noise with zero mean. Thevariances of the process noise and measurement noise may be denoted qand r, respectively. Since the variances may be required to be estimatedfor this case, the variances may be estimated from the training obtainedduring training mode.

The Kalman filter may be given by:{circumflex over (x)}(k|k)={circumflex over(x)}(k|k−1)+L(k)(y(k)−{circumflex over (x)}(k|k−1))where the notation {circumflex over (x)}(j|k) denotes the predicted orestimated state at time step j given the measurement at time step k(i.e., the measurement y(k)). The filter gain L(k) may be updatedaccording to the following equations:

$\begin{matrix}{L(k)} & {{= \frac{P\left( {k❘{k - 1}} \right)}{{P\left( {k❘{k - 1}} \right)} + r}},} \\{P\left( {k❘k} \right)} & {{= {P\left( {k❘{k - 1}} \right)\left( {1 - {L(k)}} \right)}},} \\{P\left( {{k + 1}❘k} \right)} & {= {{a^{2}{P\left( {k❘k} \right)}} + {q.}}}\end{matrix}$

The multistep Kalman predictor may be given by{circumflex over (x)}(k+j|k)=a{circumflex over (x)}(k+j−1|k)where j=1, . . . , N_(pred) which is initialized with {circumflex over(x)}(k|k) (i.e., the output of the Kalman filter). During acommunication update, the following data array may be sent to controller366:

${y_{pred}\left( {k❘k} \right)} = \begin{bmatrix}{\hat{x}\left( {k❘k} \right)} \\{\hat{x}\left( {{k + 1}❘k} \right)} \\{\hat{x}\left( {{k + 2}❘k} \right)} \\\vdots \\{\hat{x}\left( {{k + N_{pred}}❘k} \right)}\end{bmatrix}$where the first element of the array may correspond to the filteredmeasurement at the current time and the last element is the predictedvalue N_(pred) time steps into the future.

Under training mode, measurement data and the setpoint of the controlloop may be collected, if available. The outputs of the training modemay be the model parameters and noise statistics that are used for thepredictive mode. The input to the predictive mode may be the outputmeasurement (ideally, supplied every T_(s,ctrl) time steps to update thefilter) and the output may be the data array that is transmitted to thecontroller every T_(s,comm) time steps.

Still referring to FIGS. 9A-9B, other predictive models and algorithmsmay be used for predicting future values of the building variable ormeasured variable. For example, a deterministic and stochastic model maybe used. An autoregressive model may be used. An example of such apredictive model can be found in U.S. patent application Ser. No.14/717,593 titled “Building Management System for Forecasting TimeSeries Values of Building Variables” filed May 20, 2015.

Referring now to FIGS. 10A-10B, a system 1000 and process 1050 foranother new wireless transmission timing method are shown, according toanother exemplary embodiment. The systems and methods shown in FIGS.10A-10B may be performed by the system of FIG. 4. In this embodiment,predictions are made on the controller side of the system. In someembodiments, wireless device 408 continues to take measurements at anormal sampling rate, transmits a history of a predetermined length tocontroller 366, and allows controller 366 to make the predictions.System 1000 and process 1050 may include step 1002, in which a value ofa building variable is measured. In some embodiments, step 1002 may beperformed by wireless device 408. Referring to FIGS. 5B and 10A-10B,step 1002 may be performed by sensor 502. In some embodiments, step 1002is executed by another component of wireless device 408 which may not beshown. System 1000 and process 1050 may not include taking moremeasurements than transmissions. For example, if a transmission is sentonce every six minutes, it may not be necessary to measure a value moreoften than every six minutes. In other embodiments, system 1000 andprocess 1050 include taking measurements more often than transmissionsare sent. For example, if a transmission is sent every fifteen minutes,measurements may be made every minute.

In step 1004, wireless device 408 may determine whether to generate amessage. In some embodiments, wireless device 408 decides to generate anew message when a change in value occurs, when a magnitude of the errorbetween the predicted value and the later measured value of the sametime is too large, a rate of change of an error or of measurements isover a predetermined threshold, etc. In some embodiments, the generationof a new message leads to the transmission of the new message. Thetransmission of a new message may begin a new transmission interval. Forexample, a transmission interval may be four minutes long, and may spantime 1 to time 5. If a change of value is detected at time 3, a newtransmission interval may span time 3 to time 7. In some embodiments, ifa user makes a change to the system, a new message may be generated. Forexample, if a user changes a setpoint or a setting of the system, a newmessage may be generated for transmittal to the feedback controller.

The time constant of a system may change, making predictions inaccurate.For example, the frequency response of a temperature control system canchange due to a change in occupancy of a particular zone. In someembodiments, the system retunes and/or redefines the predictionalgorithm if an error commonly occurs or if an error occurs too often.In other embodiments, the system retunes and/or redefines the predictionalgorithm if new messages are sent too often or if corrections are madetoo often. For example, if a new message is being sent twice in twentycontroller update periods, the system may retune the predictionalgorithm. Retuning can occur periodically and automatically. Forexample, retuning of the algorithm may occur every five controllerupdate periods. In some embodiments, retuning occurs based on conditionssuch as a frequency of errors. For example, if an error is detectedthree times in ten controller update periods, retuning of the predictionalgorithm may occur.

System 1000 and process 1050 may include step 1006, in which a messageis generated. Referring to FIGS. 5B and 10A-10B, a message 1008 may begenerated containing measured values 1010. In some embodiments, measuredvalues 1010 may be a single value. Message 1008 may be generated suchthat the existing controller may be used without making changes to thecontrol algorithm. For example, message 1008 may be in a format that maybe read by message parser 416. In some embodiments, message 1008 may begenerated such that changes may need to be made to the controlalgorithm. Message 1008 may be generated in a format which may not bedirectly input to the controller. In some embodiments, the controller isBMS controller 366 and may be a component of BMS controller 366 such asfeedback controller 418. Message 1008 may be parsed by message parser416 such that the processed values may be input directly to feedbackcontroller 418.

Referring still to FIGS. 10A-10B, system 1000 and process 1050 mayinclude step 1012 in which message 1008 is transmitted to thecontroller. In some embodiments, the controller is BMS controller 366.In other embodiments, the controller is a component of BMS controller366 such as feedback controller 418. Step 1012 may occur through the useof wireless radio chips 514 and 516 of FIG. 5B. In some embodiments,transmission step 1012 occurs wirelessly over wireless communicationsprotocols such as ZigBee, WiFi, Bluetooth, NFC, etc. In otherembodiments, transmission step 1012 may require a physical connection ora wired connection.

System 1000 and process 1050 may further include step 1014, in whichmessage 1008 is received. In some embodiments, message 1008 istransmitted from wireless device 408 and is received by BMS controller366. Message 1008 may be transmitted through wireless radio chip 514 andreceived wireless radio chip 516. In some embodiments, message 1008 maybe received by a component of BMS controller 366.

System 1000 and process 1050 may further include step 1016, in whichmessage 1008 is parsed to obtain values. In some embodiments, step 1016is performed by BMS controller 366. In other embodiments, step 1016 isperformed by a component of BMS controller 366 such as message parser416. Message parser 416 may parse message 1008 to separate each valuefor sequential input to feedback controller 418. For example, messageparser 416 may separate measured values 1010 for input to BMS controller366. In some embodiments, message 1008 contains indicators for the orderin which the values are to be input to feedback controller 418. In otherembodiments, message parser 416 may input values to feedback controller418 in the order the values are arranged in message 1008.

System 1000 and process 1050 may continue with step 1018 in which futurevalues of the building variable (or measured variable) are predicted. Insome embodiments, step 1018 may be performed by wireless device 408.Step 1018 may be performed by low-power microcontroller 504, or acomponent of low-power microcontroller 504. In some embodiments, step1018 is performed by future value predictor 506 of wireless device 408.The algorithm for predicting the future value may use any predictionmethod. In some embodiments, filters, such as a Kalman filter may beused to predict future values of the measured variable. In otherembodiments, deterministic and stochastic models are be used.Autoregressive models may be used. Exemplary models and algorithmsdescribed for FIGS. 9A-9B apply to the systems and methods of FIGS.10A-10B as well.

Still referring to FIGS. 10A-10B, system 1000 and process 1050 mayinclude step 1020, in which the predicted and parsed values from steps1018 and 1016 respectively may be periodically and sequentially input toa feedback controller. In some embodiments, the feedback controller isfeedback controller 418. Values may be measured values 1010 and/orpredicted values. In some embodiments, the values are marked byindicators with whether each value is measured or predicted. Forexample, a value received by feedback controller 418 may be flagged as apredicted value, and may elicit additional processing in the controlalgorithm. In other embodiments, the values are not marked byindicators, and feedback controller 418 may not be aware of which valuesare measured and which are predicted. Feedback controller 418 may not beconfigured to handle predicted values, and in some embodiments, may notbe configured to identify extraneous data that is not the value neededfor the control algorithm. In some embodiments, multiple measured values1010 are received by BMS controller 366 and used to make predictions fora horizon of a predetermined length. In other embodiments, a singlemeasured value 1010 is received by BMS controller and used to makepredictions for the same horizon. Predictions may be made for a shorter,or longer horizon, and the length of the horizon may be determined by auser, or automatically by the system.

Referring now to FIG. 10B, process 1050 is shown to include step 1052,which may be an embodiment of step 1004 of FIG. 10A. Repeated figurenumbers indicate similar items or steps. Process 1050 may proceed fromstep 1002 to step 1052. In step 1052, wireless device 408 may determinewhether the transmission interval has elapsed. In some embodiments, ifthe time to the next transmission has elapsed, process 1050 may continuewith step 1006. If the time to the next transmission has not elapsed,process 1050 may continue with making another measurement of measured orbuilding variable in step 1002. Step 1052 may be performed, in someembodiments, by components of BMS controller 366. For example, step1052, in which a determination is made as to whether the time to thenext transmission has elapsed, may be performed by transmission timer508. In some embodiments, a separate processor or module of low-powermicrocontroller 504 performs step 1052. In other embodiments, a separatecomponent of wireless device 408 performs step 1052.

Configuration of Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements may bereversed or otherwise varied and the nature or number of discreteelements or positions may be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepsmay be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions may be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure may be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps maybe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

What is claimed is:
 1. A building control system comprising: a wirelessmeasurement device comprising: a sensor that measures a plurality ofvalues of an environmental variable at a location of the wirelessmeasurement device; a predictor that uses the plurality of values of theenvironmental variable to predict one or more future values of theenvironmental variable; and a first wireless radio that periodicallytransmits, at a transmission interval, a message comprising a currentvalue of the environmental variable and the one or more predicted futurevalues of the environmental variable; wherein the wireless measurementdevice compares an actual measured value of the environmental variablewith a previously-predicted value of the environmental variable andtransmits the message to the controller in response to a determinationthat the actual measured value differs from the previously-predictedvalue; and a controller comprising: a second wireless radio thatreceives the message from the wireless measurement device; a messageparser that parses the message to extract the current value and the oneor more predicted future values of the environmental variable; and afeedback controller that periodically and sequentially applies, at acontroller update interval shorter than the transmission interval, eachof the current value and the one or more predicted future values as aninput to a control algorithm that operates to control the environmentalvariable.
 2. The building control system of claim 1, wherein thewireless measurement device periodically measures the environmentalvariable at a measurement interval shorter than the transmissioninterval.
 3. The building control system of claim 1, wherein thewireless measurement device predicts a future value of the environmentalvariable for each controller update interval within the transmissioninterval.
 4. The building control system of claim 1, wherein thewireless measurement device: compares the current measured value of theenvironmental variable with a previous measured value of theenvironmental variable; determines a rate of change of the environmentalvariable between the current and previous measured value; and transmitsthe message to the controller in response to a determination that therate of change exceeds a threshold.
 5. The building control system ofclaim 1, wherein the wireless measurement device: compares an actualmeasured value of the environmental variable at a particular time with apreviously-predicted value of the environmental variable for theparticular time; determines whether a difference between the actualmeasured value and the previously-predicted value exceeds a threshold;and transmits the message to the controller in response to adetermination that the difference between the actual measured value andthe previously-predicted value exceeds the threshold.
 6. The buildingcontrol system of claim 1, wherein the wireless measurement device:compares an actual measured value of the environmental variable with atleast one of a previously-predicted value of the environmental variableand a previously-measured value of the environmental variable; andredefines a prediction algorithm used by the predictor in response to atleast one of: a difference between the actual measured value of theenvironmental variable and the previously-predicted value of theenvironmental variable exceeding a threshold; and a rate of change ofthe environmental variable exceeding a threshold.
 7. A method forcontrolling an environmental variable in a building control system, themethod comprising: measuring a plurality of values of the environmentalvariable using a sensor of a wireless measurement device; predicting, bythe wireless measurement device, one or more future values of theenvironmental variable based on the plurality of values; periodicallytransmitting, at a transmission interval, a message from the wirelessmeasurement device to a controller, the message comprising a currentvalue of the environmental variable and the one or more predicted futurevalues of the environmental variable; comparing an actual measured valueof the environmental variable at a particular time with apreviously-predicted value of the environmental variable for theparticular time; determining whether a difference between the actualmeasured value and the previously-predicted value exceeds a threshold;transmitting the message to the controller in response to adetermination that the difference between the actual measured value andthe previously-predicted value exceeds the threshold; parsing themessage at the controller to extract the current value and the one ormore predicted future values of the environmental variable; andperiodically and sequentially applying, at a controller update intervalshorter than the transmission interval, each of the current value andthe one or more predicted future values as an input to a controlalgorithm that operates to control the environmental variable.
 8. Themethod of claim 7, wherein measuring the plurality of values of theenvironmental variable comprises periodically measuring theenvironmental variable at a measurement interval shorter than thetransmission interval.
 9. The method of claim 7, further comprising:comparing the current measured value of the environmental variable witha previous measured value of the environmental variable; determining arate of change of the environmental variable between the current andprevious measured value; and transmitting the message to the controllerin response to a determination that the rate of change exceeds athreshold.
 10. The method of claim 7, wherein predicting theenvironmental variable comprises predicting a future value of theenvironmental variable for each controller update interval within thetransmission interval.
 11. The method of claim 7, further comprising:comparing an actual measured value of the environmental variable with apreviously-predicted value of the environmental variable; and adjustingthe transmission interval based on one of: a difference between theactual measured value of the environmental variable and thepreviously-predicted value of the environmental variable; and a rate ofchange of the environmental variable.
 12. The method of claim 7, furthercomprising: comparing an actual measured value of the environmentalvariable with the previously-predicted value of the environmentalvariable; and redefining a prediction algorithm used by the wirelessmeasurement device based on one of: a difference between the actualmeasured value of the environmental variable and thepreviously-predicted value of the environmental variable; and a rate ofchange of the value of the environmental variable.
 13. A wirelessmeasurement device comprising: a sensor that measures a plurality ofvalues of an environmental variable at a location of the wirelessmeasurement device; a predictor that uses the plurality of values of theenvironmental variable to predict one or more future values of theenvironmental variable; a first wireless radio that periodicallytransmits to a controller, at a transmission interval, a messagecomprising a current value of the environmental variable and the one ormore predicted future values of the environmental variable; atransmission timer that controls the transmission interval, wherein thecontroller is configured to control the environmental variable using themessage and a control algorithm; and a comparator that compares anactual measured value of the environmental variable with apreviously-predicted value of the environmental variable and determineswhether a difference between the actual measured value and thepreviously-predicted value exceeds a threshold, wherein the message istransmitted to the controller in response to a determination that theactual measured value differs from the previously-predicted value by anamount in excess of the threshold.
 14. The wireless measurement deviceof claim 13, configured to periodically measure the environmentalvariable at a measurement interval shorter than the transmissioninterval.
 15. The wireless measurement device of claim 13, wherein thepredictor predicts a future value of the environmental variable for eachof a plurality of controller update intervals within the transmissioninterval.
 16. The wireless measurement device of claim 13, furthercomprising: a comparator that compares a current measured value of theenvironmental variable with a previous measured value of theenvironmental variable and determines whether a rate of change betweenthe current measured value and the previous measured value exceeds athreshold, wherein the message is transmitted to the controller inresponse to a determination that the rate of change exceeds thethreshold.
 17. The wireless measurement device of claim 13, furthercomprising: a comparator that compares an actual measured value of theenvironmental variable with a previously-predicted value of theenvironmental variable, wherein the transmission timer adjusts thetransmission interval based on one of: a difference between the actualmeasured value of the environmental variable and thepreviously-predicted value of the environmental variable; and a rate ofchange of the environmental variable.